de7ee4383e
V1 paged_attention (decode ≤ 8192): Fix: head_mapping int→Tensor conversion. VERIFIED: matches manual attention, max diff < 0.001. Perf: 0.034ms (256 tok), 0.059ms (1K), 0.169ms (4K), 0.272ms (8K). V2 paged_attention (decode > 8192): Native V2 kernel EXISTS (ixf_F.vllm_single_query_cached_kv_attention_v2) but produces INCORRECT output (diff=1.28 vs V1 on same data). Using Python V2 fallback (paged_attention_v2_pytorch.py) for now. The native V2 expects [B,H,bs,d] layout (confirmed) but the output values don't match even with correct layout conversion. Prefill (flash_attn_func): VERIFIED: ixf_F.flash_attn_func(q, k, v, causal=True) works with head_dim=256 and GQA (num_kv_heads=4). Patched into xformers.py as first-attempt before _run_sdpa_fallback. Triton: symlinked /usr/local/lib/ → /usr/local/corex/lib64/ for import.